Drillboards: Adaptive Visualization Dashboards for Dynamic Personalization of Visualization Experiences

Fuente: arXiv
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Main Authors: Shin, Sungbok, Na, Inyoup, Elmqvist, Niklas
Format: Preprint
Published: 2024
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author Shin, Sungbok
Na, Inyoup
Elmqvist, Niklas
author_facet Shin, Sungbok
Na, Inyoup
Elmqvist, Niklas
contents We present drillboards, a technique for adaptive visualization dashboards consisting of a hierarchy of coordinated charts that the user can drill down to reach a desired level of detail depending on their expertise, interest, and desired effort. This functionality allows different users to personalize the same dashboard to their specific needs and expertise. The technique is based on a formal vocabulary of chart representations and rules for merging multiple charts of different types and data into single composite representations. The drillboard hierarchy is created by iteratively applying these rules starting from a baseline dashboard, with each consecutive operation yielding a new dashboard with fewer charts and progressively more abstract and simplified views. We also present an authoring tool for building drillboards and show how experts users can use to build up and deliver personalized experiences to a wide audience. Our evaluation asked three domain experts to author drillboards for their own datasets, which we then showed to casual end-users with favorable outcomes.
format Preprint
id arxiv_https___arxiv_org_abs_2410_12744
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Drillboards: Adaptive Visualization Dashboards for Dynamic Personalization of Visualization Experiences
Shin, Sungbok
Na, Inyoup
Elmqvist, Niklas
Human-Computer Interaction
We present drillboards, a technique for adaptive visualization dashboards consisting of a hierarchy of coordinated charts that the user can drill down to reach a desired level of detail depending on their expertise, interest, and desired effort. This functionality allows different users to personalize the same dashboard to their specific needs and expertise. The technique is based on a formal vocabulary of chart representations and rules for merging multiple charts of different types and data into single composite representations. The drillboard hierarchy is created by iteratively applying these rules starting from a baseline dashboard, with each consecutive operation yielding a new dashboard with fewer charts and progressively more abstract and simplified views. We also present an authoring tool for building drillboards and show how experts users can use to build up and deliver personalized experiences to a wide audience. Our evaluation asked three domain experts to author drillboards for their own datasets, which we then showed to casual end-users with favorable outcomes.
title Drillboards: Adaptive Visualization Dashboards for Dynamic Personalization of Visualization Experiences
topic Human-Computer Interaction
url https://arxiv.org/abs/2410.12744